Data Science

Data Science 101

Data science happens to be quite a broad topic. There are various answers to the question- “What is Data science?” Data science is known to be such a practice which involves a lot of points. Some of the key points that are focused upon are-

  1. It involves formulation of various hypothesizes and answering to known and unknown problems that generates a business drive.
  2. Working on the provided or existing set of data and also looking up for various analyses to collect data that is relevant.
  3. After the extraction of relevant data, it needs to be summarized in such a way that any common man can understand and put it into use. This helps in generating an effective business drive.

Data science is known to be a perfect blend of various disciplines such as algorithm development, data inference and technologies. These are essential to solve critical problems analytically and generate values.

We know, in the core of any warehouse, data is the uncrowned king. Raw data is streamed and stored at the organization’s warehouses from where the relevant information is extrapolated and important points are learnt. There are various steps with the help of which information is uncovered from the raw streamed data.

  1. First the information arrives at the data warehouse.
  2. The next process is about discovering the insight of data. In this step, it involves the extraction of information from the raw data. Data scientists bury themselves in the grain-level to mine and uncover information that will help organizations make witty business decisions. This involves solving complex problems, understanding the trends and complex behaviors. Thus, they surface the hidden inferences that are essential.
    Basically data scientists act like detectives while mining out the insights from raw form of data. They investigate and find leads. They follow them up which helps them to understand the complex problem statements or patterns. Hence, they breakthrough through the maze of data and come up with relevant information.
  3. Next step is development of “Data product”. What is data product? Data product is the asset to technology that uses data as an input. It returns results that are algorithmically generated. This is different than data insight. In case of data insight, the outcome generated helps an executive or rather advices an executive to make a smart business move. When it comes to data product, it a technical function that actually sums up the algorithm and can be directly integrated into the core applications of the organization.

From data insight to data product, data scientist plays a vital role in all of this. Fabricating algorithms, testing, refining the products, deployment of the same into core process, data scientists play the central role and can be termed as the head of the system.

What does it take to be a data scientist?

No, one doesn’t have to become Sherlock Holmes. Data science is known to be a perfect blend of three skills.

  1. Mathematical expertise: this skill for a data scientist is like preaching the choir. As a data scientist, a person should have the ability to see through the problems, patterns, dimensions and correlations. A myth that follows is that data science is all about statistics. But it isn’t so. Statistics are an important part, not the ONLY part.
  2. Technology and hacking: here hacking refers to breaking into the problem with high level of creativity using adequate technical skills. They have to build smart things and come up with clever solutions.
  3. Strong business acumen: as a data scientist, one has to think out of the box. This skill helps them to distinguish and manage business goals and projects effectively.

Another question that comes up – “Is an analyst and data scientist synonymous”?

Well, no. They are certainly mutual but not synonymous. An analyst is a person who just looks at the provided data and gain insights. They generally work on the surface i.e. the database level and also the report level. But a data scientist is a person who works with the raw data who can derive o rather breakthrough the insights and generate data product.

For any company, the secret recipe to success is data science.